Failure Forewarning in NPP Equipment NERI2000-109 Final Project Report

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The objective of this project is forewarning of machine failures in critical equipment at next-generation nuclear power plants (NPP). Test data were provided by two collaborating institutions: Duke Engineering and Services (first project year), and the Pennsylvania State University (Applied Research Laboratory) during the second and third project years. New nonlinear methods were developed and applied successfully to extract forewarning trends from process-indicative, time-serial data for timely, condition-based maintenance. Anticipation of failures in critical equipment at next-generation NPP will improve the scheduling of maintenance activities to minimize safety concerns, unscheduled non-productive downtime, and collateral damage due to unexpected failures. This ... continued below

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Hively, LM March 26, 2004.

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The objective of this project is forewarning of machine failures in critical equipment at next-generation nuclear power plants (NPP). Test data were provided by two collaborating institutions: Duke Engineering and Services (first project year), and the Pennsylvania State University (Applied Research Laboratory) during the second and third project years. New nonlinear methods were developed and applied successfully to extract forewarning trends from process-indicative, time-serial data for timely, condition-based maintenance. Anticipation of failures in critical equipment at next-generation NPP will improve the scheduling of maintenance activities to minimize safety concerns, unscheduled non-productive downtime, and collateral damage due to unexpected failures. This approach provides significant economic benefit, and is expected to improve public acceptance of nuclear power. The approach is a multi-tiered, model-independent, and data-driven analysis that uses ORNL's novel nonlinear method to extract forewarning of machine failures from appropriate data. The first tier of the analysis provides a robust choice for the process-indicative data. The second tier rejects data of inadequate quality. The third tier removes signal artifacts that would otherwise confound the analysis, while retaining the relevant nonlinear dynamics. The fourth tier converts the artifact-filtered time-serial data into a geometric representation, that is then transformed to a discrete distribution function (DF). This method allows for noisy, finite-length datasets. The fifth tier obtains dissimilarity measures (DM) between the nominal-state DF and subsequent test-state DFs. Forewarning of a machine failure is indicated by several successive occurrences of the DM above a threshold, or by a statistically significant trend in the DM. This paradigm yields robust nonlinear signatures of degradation and its progression, allowing earlier and more accurate detection of the machine failure.

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  • Report No.: ORNL/TM-2003/222
  • Grant Number: DE-AC05-00OR22725
  • DOI: 10.2172/885692 | External Link
  • Office of Scientific & Technical Information Report Number: 885692
  • Archival Resource Key: ark:/67531/metadc892398

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Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • March 26, 2004

Added to The UNT Digital Library

  • Sept. 23, 2016, 2:42 p.m.

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  • Dec. 9, 2016, 4:23 p.m.

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Hively, LM. Failure Forewarning in NPP Equipment NERI2000-109 Final Project Report, report, March 26, 2004; [Tennessee]. (digital.library.unt.edu/ark:/67531/metadc892398/: accessed December 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.